• Ma, P., Karagiannis, G., Konomi, B.A., Asher, T.G., Toro, G.R. & Cox, A.T. (2022) Multifidelity computer model emulation with high-dimensional output: An application to storm surge. Journal of the Royal Statistical Society: Series C, 1–23
  • Chang, W., Konomi, B. A., Karagiannis, G., Guan, Y., & Haran, M. (2022). Ice Model Calibration Using Semi-continuous Spatial Data. Annals of Applied Statistics.
  • Karagiannis, G., Hou, Z., Huang, M., & Lin, G. (2022). Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework. Computation, 10(5), 72.
  • Cheng, S., Konomi, B. A., Matthews, J. L., Karagiannis, G., & Kang, E. L. (2021). Hierarchical Bayesian nearest neighbor co-kriging Gaussian process models; an application to intersatellite calibration. Spatial Statistics, 100516.
  • Konomi, B. A., & Karagiannis, G. (2020). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics., 1-31.
  • Karagiannis, G., Hao, W., & Lin, G. (2020) Calibrations and validations of biological models with an application on the renal fibrosis. International Journal for Numerical Methods in Biomedical Engineering, e3329.
  • Karagiannis, G., Konomi, B. A., & Lin, G. (2019). On the Bayesian calibration of expensive computer models with input dependent parameters, Spatial Statistics
  • Alamaniotis, M., & Karagiannis, G. (2019). Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed. IET Renewable Power Generation. – Special Issue from Medpower 2018.
  • Karagiannis, G., Konomi, B. A., Lin, G., & Liang, F. (2017). Parallel and interacting stochastic approximation annealing algorithms for global optimisation. Statistics and Computing, 27(4):927–945.
  • Konomi, B. A., Karagiannis, G., Lai, K., & Lin, G. (2017). Bayesian treed calibration: An application to carbon capture with AX sorbent. Journal of the American Statistical Association, 112(517):37-53.
  • Karagiannis, G., & Lin, G. (2017). On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. Journal of Computational Physics, 342:139 - 160.
  • Alamaniotis, M., & Karagiannis, G. (2017). Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 5(3), 1-14.
  • Karagiannis, G., Konomi, B. A., & Lin, G. (2015). A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs. Journal of Computational Physics, 284:528 - 546.
  • Konomi, B. A., Karagiannis, G., & Lin, G. (2015). On the Bayesian treed multivariate Gaussian process with linear model of coregionalization. Journal of Statistical Planning and Inference, 157-158:1 - 15.
  • Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., & Lin, G. (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics, 300:623 - 642.
  • Karagiannis, G. , & Lin, G. (2014). Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs. Journal of Computational Physics, 259:114 - 134.
  • Konomi, B. A., Karagiannis, G., Sarkar, A., Sun, X., & Lin, G. (2014). Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit. Technometrics, 56(2):145- 158.
  • Karagiannis, G., & Andrieu, C. (2013). Annealed importance sampling reversible jump MCMC algorithms. Journal of Computational and Graphical Statistics, 22(3):623-648.


  • Deng, W., Feng, Q., Karagiannis, G., Lin, G., & Liang, F. (2021). Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. International Conference on Learning Representations (ICLR'21).
  • Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G. (2021, June). Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs. In 2021 IEEE Madrid PowerTech (pp. 1-6). IEEE.
  • Alamaniotis, M., & Karagiannis, G. (2019, June). Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Assimilation. In 2019 IEEE Milan PowerTech (pp. 1-6). IEEE.
  • Alamaniotis, M. & Karagiannis, G. (2018), Genetic Driven Multi-Relevance Vector Regression Forecasting of Hourly Wind Speed in Smart Power Systems, The Ninth Annual IEEE PES Conference on Innovative Smart Grid Technology North America. Washington, DC
  • Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H. & Karagiannis, G. (2017), A Three-Stage Scheme for Consumers' Partitioning Using Hierarchical Clustering Algorithm, 8th International Conference on Information, Systems and Applications (IISA). Larnaca, Cyprus, 6.

  • Karagiannis G.P. (2022) Introduction to Bayesian Statistical Inference. In: Aslett L.J.M., Coolen F.P.A., De Bock J. (eds) Uncertainty in Engineering. SpringerBriefs in Statistics. Springer, Cham.

  • Qiu, T., Karagiannis, G., & Lin, G. (August 4, 2016). Model Selection Using Gaussian Mixture Models and Parallel Computing, The Summer Undergraduate Research Fellowship (SURF) Symposium, Paper 142. [Link]
  • Karagiannis, G. (2011). AISRJMCMC-Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm: a pseudo-marginal reversible jump MCMC algorithm (Doctoral dissertation, University of Bristol). [Link]

  • Karagiannis, G., Andrieu, C. (2009, 2016). Drawing samples from inverse Wishart distributions conditioning on the 1st block diagonal sub-matrix; with an application to variable selection on a GLMM model with nested random effects, GitHub repository manuscript, [Link]


  • SIAM Conference on Uncertainty Quantification (UQ22), Atlanta, GA / USA

  • UC Mathematics Department Colloquium, University of Cincinnati, OH / USA

  • Departmental seminar, Department of Electrical and Computer Engineering, UTSA, TX/USA
  • Statistics seminar, Department of statistics, AUEB, Greece
  • SAMSI program in Model Uncertainty: Mathematical and Statistical, SAMSI, NC / USA
  • International Society for Bayesian Analysis meeting (ISBA2018), Edinburgh, UK
  • SIAM Conference on Uncertainty Quantification (UQ18), Garden Grove, CA / USA
  • Workshop on the Current Trends and Challenges in Data Science and Uncertainty Quantification, Purdue University, IN / USA
  • ACMS Department Colloquium, University of Notre Dame, IN / USA
  • ASA Joint Statistical Meetings, Seattle, WA / USA
  • IdeaLab 2015, ICERM program, Brown University, RI / USA
  • 22nd ASA/IMS 2015 Spring Research Conference (SRC), OH / USA

  • UC Mathematics Department Colloquium, University of Cincinnati, OH / USA

  • PNNL Post-doc Symposium, Richland, WA / USA
  • CRiSM model uncertainty workshop, University of Warwick, UK
  • Greek Stochastics a’ Monte Carlo: Probability and Methods, Lefkada, Greece
  • Research students conference in probability and statistics, University of Lancaster, UK
  • Research students conference in probability and statistics, University of Nottingham, UK